High resolution parametric description of slow wave sleep

Objective: to establish the relationship between the presence of cognitive disorders in patients with arterial hypertension and changes in EEG, to assess the dynamics of these changes against the background of various modes of cerebroprotective therapy.Materials and methods: the study involved 92 people with arterial hypertension, whose average age was 63 ± 8.2 years. The research was carried out on the device “Encephalan-EEGR-19/26”. To assess cognitive functions, patients were tested using the MoСA test. Patients with cognitive impairment were divided into three groups of dynamic monitoring with diff erent modes of cerebroprotective therapy.Results: non-specifi c patterns in the slow-wave range were registered in patients with cognitive impairment during visual EEG analysis. In the quantitative analysis of the EEG revealed changes in the frequency and amplitude of the alpha rhythm, the power variation on the basic rhythms, the reduction of the total strength of the rhythms, the increase in relative power of slow rhythms in the frontal leads to the total power of the rhythms. After the treatment, most patients showed an increase in scores on the “Montreal scale”, a decrease in anxiety and depression on the” Hospital scale”, and an increase in the SF-36 index. Quantitative EEG analysis revealed positive dynamics comparable to the clinic and test data. The most favorable EEG dynamics was registered in groups of patients receiving neuroprotective and combined therapy.Conclusions: the results obtained indicate the diagnostic value of quantitative EEG analysis and the feasibility of adding drugs that improve the metabolism and blood supply to the brain to standard antihypertensive therapy.

EEG in the healthy term newborn within 12 hours of birth

Dexmedetomidine, a selective alpha(2)-adrenoceptor agonist, induces a unique, sleep-like state of sedation. The objective of the present work was to study human electroencephalogram (EEG) sleep spindles during dexmedetomidine sedation and compare them with spindles during normal physiological sleep, to test the hypothesis that dexmedetomidine exerts its effects via normal sleep-promoting pathways. EEG was continuously recorded from a bipolar frontopolar-laterofrontal derivation with Entropy Module (GE Healthcare) during light and deep dexmedetomidine sedation (target-controlled infusions set at 0.5 and 3.2 ng/ml) in 11 healthy subjects, and during physiological sleep in 10 healthy control subjects. Sleep spindles were visually scored and quantitatively analyzed for density, duration, amplitude (band-pass filtering) and frequency content (matching pursuit approach), and compared between the two groups. In visual analysis, EEG activity during dexmedetomidine sedation was similar to physiological stage 2 (S2) sleep with slight to moderate amount of slow-wave activity and abundant sleep spindle activity. In quantitative EEG analyses, sleep spindles were similar during dexmedetomidine sedation and normal sleep. No statistically significant differences were found in spindle density, amplitude or frequency content, but the spindles during dexmedetomidine sedation had longer duration (mean 1.11 s, SD 0.14 s) than spindles in normal sleep (mean 0.88 s, SD 0.14 s; P=0.0014). Analysis of sleep spindles shows that dexmedetomidine produces a state closely resembling physiological S2 sleep in humans, which gives further support to earlier experimental evidence for activation of normal non-rapid eye movement sleep-promoting pathways by this sedative agent.

Several topographic mapping studies of electroencephalographic (EEG) power spectra have reported increased slow (delta) activity in the frontal regions of schizophrenic patients. Using supraorbital and lateral canthus electrodes to detect eye movement, we deleted EEG epochs during eye movement in 15 medication-free patients with schizophrenia and in 13 normal control subjects. Power spectral analysis of the 28-channel EEG demonstrated a diffuse mild increase in delta activity in schizophrenic patients compared with normal control subjects but no tendency for frontal localization of this slow activity. There were no differences between schizophrenic patients and normal control subjects in other frequency bands. These results, which replicate earlier findings of increased delta activity in schizophrenia, emphasize the importance of excluding the slow activity due to eye movement in the comparisons of summed EEG spectra. This emphasis can best be ensured by equating the summed spectra from extraocular movement channels of experimental and control groups.

The National Sleep Research Resource (NSRR, www.sleepdata.org) features thousands of polysomnograms (PSGs) that can be analyzed for further understanding how variations in physiological signals associate with health outcomes. Quantitative EEG analysis may help characterize physiological variation. However, analysis of large datasets collected in uncontrolled settings requires a robust pipeline including artifact detectors. To promote community-wide use of PSG data, we developed an open-source, automated pipeline for spectral analysis of sleep EEGs and tested the level of agreement with traditional analysis. We used data from the C3-A2 EEG lead in a sample of PSGs from 161 women participating in the Study of Osteoporotic Fractures. The traditional approach used manual artifact removal on 4-s basis and application of commercial spectral analysis software. Automated analysis included spectral power-based artifact detection on 30-s basis and generation of summary figures for adjudication. We compared automatic and manual artifact detection epoch-by-epoch and then compared the average EEG spectral power density in six frequency bands obtained with the two approaches using correlation analysis, Bland-Altman plots and Wilcoxon test. The automated artifact detection algorithm had high specificity (96.8 to 99.4% in NREM, 96.9 to 99.1% in REM depending on the criterion for comparing 4-s with 30-s epochs) but lower sensitivity (26.7 to 38.1% in NREM, 9.1 to 27.4% in REM). However, we found no clinically or statistically significant differences in power density values, and results were highly correlated (Spearman’s r>0.99). Large artifacts (total power >99th percentile) were removed with sensitivity up to 90.9% in NREM, 87.7% in REM, specificity 96.6% and 96.9%. The automated pipeline generated similar results to those obtained with standard approach, while reducing analysis time 100-fold. This Matlab toolset, publicly available on the NSRR website, can be used to analyze thousands of recordings, allowing for its application in genetics and epidemiological research. The work presented in this paper was funded by: NIH R24 HL114473, 1R01HL083075-01, R01HL098433, R01 HL098433-02S1, 1U34HL105277-01, 1R01HL110068-01A1 1R01HL113338-01, R21 HL108226, P20 NS076965, R01 HL109493 and R03MH108908; and a research agreement with the Emma B. Bradley Hospital/Brown University.

For the classification of sleep stages, international standards based on visual EEG analysis have been established and are in common use, although we are well aware of their limitations. Several authors have suggested different procedures for classifying the stages of vigilance during the waking stages. No universally accepted paradigm, however, has yet been developed. The proposed vigilance classification procedures are based either on visual or automatic analysis procedures. Even though the EEG activity and patterns that reflect vigilance changes have been identified and described as indicators of the state of alertness, opinion is divided on how these should be combined in a vigilance classification rule. Automatic methods, on the other hand, have up to now used only part of the information available, the relationship of which to vigilance indicators has only been partially explored. The COMSTAT (Dept. of Computation and Statistics, AFB-Arzneimittelforschung, Berlin, FRG) rule combines visual and automatic analysis procedures. Different vigilance-dependent EEG patterns, such as the proportion of occipital background rhythm under resting conditions and its replacement by either faster or slower waves, the frequency range of the occipital rhythm and the anteriorization phenomena, have been used as information for a latent class analysis (LCA5) with 5 classes (stages of vigilance). There is a high correlation between the results of the LCA5 with visual classification rules made by experts. Using a robust discriminant analysis function which takes into account prior probabilities of the classes, and with a linear cost function for misclassification, an automatic rule with power spectrum variables was fitted to the results of the LCA5. Reclassification and split-half classification showed a high overlap between LCA5 and automatic classification. The result of this procedure is a new vigilance classification rule that is based on an objective mathematical rationale for the combination of different vigilance-indicative EEG activities and patterns but which can be applied to power-spectral estimators in an automatic EEG analysis procedure.

To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 × 7™) consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report™). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20–95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen’s kappa: 0.72) between the Somnolyzer 24 × 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen’s kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 × 7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen’s kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 × 7 and demonstrated its applicability in clinical routine and sleep studies.

To study the role of GABA-ergic mechanisms in sleep regulation, the combined action of 40 h sleep deprivation and either 20 mg zolpidem or placebo on the sleep electroencephalogram (EEG) were investigated by quantitative EEG analysis in eight young men who participated in a positron emission tomography study. Compared with baseline, sleep deprivation increased low-frequency (1.25-7.0 Hz) EEG power in non-rapid eye movement (NREM) sleep in the placebo night. After administration of zolpidem, power in the 3.75-10.0 Hz range and 14. 25-16.0 Hz band was reduced. The largest decrease was observed in the theta band. Comparison with placebo revealed that zolpidem attenuated power in the entire 1.75-11.0 Hz range. The plasma concentration of zolpidem at 4.5 h after intake showed a positive correlation with the drug-induced difference in power from placebo in the 14.25-16.0 Hz band. Regional EEG analysis based on bipolar derivations along the antero-posterior axis disclosed, for NREM sleep, a drug-induced posterior shift of power in the frequency range of 7.75-9.75 Hz. Zolpidem did not affect rapid eye movemnt sleep spectra. We conclude that sleep deprivation and agonistic modulation of GABAA receptors have separate and additive effects on power spectra and that their effects are mediated by different neurophysiological mechanisms.

Conventionally, polysomnographic recordings are classified according to the rules published in 1968 by Rechtschaffen and Kales (R&K). The present paper describes an automatic classification system embedded in an e-health solution that has been developed and validated in a large database of healthy controls and sleep disturbed patients. The Somnolyzer 24x7 adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, slow and rapid eye movements), a feature matrix plausibility check, a classifier designed as an expert system and a rule-based smoothing procedure for the start and the end of stages REM and 2. The validation based on 286 recordings in both normal healthy subjects aged 20 to 95 years and patients suffering from organic or nonorganic sleep disorders demonstrated an overall epoch-by-epoch agreement of 80% (Cohen's Kappa: 0.72) between the Somnolyzer 24x7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen's Kappa: 0.68) between two human experts. Two Somnolyzer 24x7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen's Kappa: 0.991). Moreover, correlation analysis in R&K derived target variables revealed similar -- in 36 out of 38 variables even higher -- relationships between Somnolyzer 24x7 and expert evaluations as compared to the concordance between two human experts. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24x7 both, on the epoch-by-epoch and on the target variable level. These results demonstrate the applicability of the Somnolyzer 24x7 evaluation in clinical routine and sleep studies.

To the Editor: We read the article by Adams et al. [1] with great interest since we have more than a 4-year experience using the CIMON automated electroencephalographic (EEG) analysis program. As we have not experienced the high false-positive rate noted in their study, we were initially perplexed as to how their results were possible. This feeling is further highlighted by the work of Jonkman et al. [2], who compared the results of cerebral blood flow studies, conventional EEG analysis, and neurometric EEG analysis in patients having a history of stroke or transient ischemic attacks using univariate and multivariate parameters. With respect to accurate prediction of patients having a positive history of previous ischemic events, the neurometric EEG analysis approach showed a 50% improvement over visual analysis of conventional EEG. The false-positive rate derived from a concurrent control (normal) population was 3%. These results stand in stark contrast to those obtained for studies carried out under general anesthesia [2,3]. This prompts then a consideration of the sensitivity and specificity of the current implementation of the automated neurometric analytic approach used in the CIMON program. With regard to sensitivity, there is ample evidence (Harvey Edmonds, Department of Anesthesiology, University of Louisville Medical School, personal communication) that CIMON detects ischemia when an alternate method is used to herald its occurrence (i.e., transcranial infrared spectroscopy and transcranial Doppler). It is probable that Adams et al. failed to detect ischemia because at their time of measurement there was none. Thus, the methodology used by CIMON is probably not discredited on this account. Specificity, however, is another story. Their approach was to collect a baseline of single epochs arriving at an array of mean frequency band amplitudes. A standard deviation was accordingly calculated for each parameter mean based on an average of 24 single epochs. They then compared sliding averaged updates (consisting of 3, 10, and 20 averaged single epochs). As their epoch count increased from three to 20, their false-positives likewise increased (see Table 1in [1]). Arithmetically, the results represented in Table 1of the article by Adams et al. are anomalous. The mean value of relative delta lasting for a minimum of 5 min (the definition of cerebral dysfunction) taken across updated subsets consisting of sliding averages of 10 to 20 epochs cannot occur more frequently than the occurrences across a subset consisting of a sliding average of three epochs. This is because one is comparing the same EEG populations to each other (absence of ischemia, anesthetic, temperature, and ETCO2 change). As the number of epochs increases in the update (of any sample of sliding averages), its mean should be closer to the self-norm mean. We have simulated the authors' use of the CIMON program in a series of normal anesthetized patients and have confirmed their results. Their approach, however, differs from our use of the CIMON program, as we use sliding averages having the same number of epochs as the updates to generate a self-norm. In our experience, this markedly reduces the false-positives, which occur briefly, never lasting for more than 1-2 min. Such disparate results suggest a problem in the implementation of the software. The manufacturer of the software (Cadwell Laboratories, Inc., Kennewick, WA) has been advised of this problem and is working to correct it. Nevertheless, the CIMON program is an experimental implementation of an automated use of neurometric analysis, and studies like that by Adams et al. are important and useful for detecting errors in its computerized implementation. We therefore agree with the authors that caution is advised when using automated programs for analyzing EEG. This, however, in no way detracts from the neurometric approach; it detracts only from its current implementation. Lavern D. Gugino, PhD, MD Linda S. Aglio, MD Department of Anaesthesia Harvard Medical School Boston, MA 02115 E. Roy John, PhD Brain Research Laboratories New York, NY 10016

A high rate of (temporal) EEG abnormalities within the group of transsexual patients has been previously described. These reports however were based on visual EEG analyses, which were not sufficiently statistically supported. It therefore seemed necessary to test this observations by utilizing quantitative frequency EEG analysis by using a larger group of transsexual (n=33m)

Background: Reports on the effects of focal hemispheric damage on sleep EEG are rare and contradictory. Patients and Methods: Twenty patients (mean age ± SD 53 ± 14 years) with a first acute hemispheric stroke and no sleep apnea were studied. Stroke severity [National Institute of Health Stroke Scale (NIHSS)], volume (diffusion-weighted brain MRI), and short-term outcome (Rankin score) were assessed. Within the first 8 days after stroke onset, 1–3 sleep EEG recordings per patient were performed. Sleep scoring and spectral analysis were based on the central derivation of the healthy hemisphere. Data were compared with those of 10 age-matched and gender-matched hospitalized controls with no brain damage and no sleep apnea. Results: Stroke patients had higher amounts of wakefulness after sleep onset (112 ± 53 min vs. 60 ± 38 min, p < 0.05) and a lower sleep efficiency (76 ± 10% vs. 86 ± 8%, p < 0.05) than controls. Time spent in slow-wave sleep (SWS) and rapid eye movement (REM) sleep and total sleep time were lower in stroke patients, but differences were not significant. A positive correlation was found between the amount of SWS and stroke volume (r = 0.79). The slow-wave activity (SWA) ratio NREM sleep/wakefulness was lower in patients than in controls (p < 0.05), and correlated with NIHSS (r = –0.47). Conclusion: Acute hemispheric stroke is accompanied by alterations of sleep EEG over the healthy hemisphere that correlate with stroke volume and outcome. The increased SWA during wakefulness and SWS over the healthy hemisphere contralaterally to large strokes may reflect neuronal hypometabolism induced transhemispherically (diaschisis).

Neuronal Dysfunction and Schizophrenia Symptoms

The aim of this study was to investigate topographic changes in electroencephalographic (EEG) power and frequency induced by abdominal surgery during anesthesia with 0.6% or 1.2% isoflurane in 66% nitrous oxide. Forty-six patients (aged 41 +/- 13 yr) scheduled for elective abdominal surgery were studied. The trachea of each patient was intubated and the lungs ventilated. Patients were randomly assigned to one of four groups: anesthesia was maintained with 0.6% (group 1, n = 12; group 2, n = 11) or 1.2% end-tidal isoflurane (group 3, n = 12; group 4, n = 11) in 66% nitrous oxide. Data were recorded over 20 min. Groups 1 and 3 were studied without surgery (as controls). In groups 2 and 4 recording was started 6 min before skin incision. The EEG was acquired via 17 scalp electrodes placed in standard International 10-20 locations (reference Cz). Absolute and relative power densities were calculated in selected frequency bands. EEG maps of spectral power densities were coded according to a continuous color spectrum. During baseline recordings, alpha activity was dominant at frontal areas in groups 1 and 2. In comparison, in groups 3 and 4, delta and theta activities were dominant at frontal leads. In group 2, the start of surgery resulted in increases in delta activity and decreases in alpha activity that were most dominant frontally (delta +181% and alpha -61%, F3). The delta shift was attenuated at 1.2% isoflurane (group 4, delta +44%, F3), but decreases in alpha activity (-53%, F3) were comparable to those in group 2. The EEG response in all frequency bands was attenuated at parietotemporal recording sites at both isoflurane concentrations. The current data demonstrate graded EEG responses induced by abdominal surgery during anesthesia with 0.6% or 1.2% isoflurane in 66% nitrous oxide. Spatial heterogeneities in absolute spectral power densities were reflected by color changes in the EEG maps. The topographic EEG analysis indicates that these changes were most dominant at frontal areas. The increases in delta and decreases in alpha activities may be related to intraoperative "paradoxical" electrophysiologic arousal phenomena.

Location specific sleep spindle activity in the early visual areas and perceptual learning